Summary
The oil-and-gas industry is entering an era of “big data” because of the huge number of wells drilled with the rapid development of unconventional oil-and-gas reservoirs during the past decade. The massive amount of data generated presents a great opportunity for the industry to use data-analysis tools to help make informed decisions. The main challenge is the lack of the application of effective and efficient data-analysis tools to analyze and extract useful information for the decision-making process from the enormous amount of data available. In developing tight shale reservoirs, it is critical to have an optimal drilling strategy, thereby minimizing the risk of drilling in areas that would result in low-yield wells. The objective of this study is to develop an effective data-analysis tool capable of dealing with big and complicated data sets to identify hot zones in tight shale reservoirs with the potential to yield highly productive wells. The proposed tool is developed on the basis of nonparametric smoothing models, which are superior to the traditional multiple-linear-regression (MLR) models in both the predictive power and the ability to deal with nonlinear, higher-order variable interactions. This data-analysis tool is capable of handling one response variable and multiple predictor variables. To validate our tool, we used two real data sets—one with 249 tight oil horizontal wells from the Middle Bakken and the other with 2,064 shale gas horizontal wells from the Marcellus Shale. Results from the two case studies revealed that our tool not only can achieve much better predictive power than the traditional MLR models on identifying hot zones in the tight shale reservoirs but also can provide guidance on developing the optimal drilling and completion strategies (e.g., well length and depth, amount of proppant and water injected). By comparing results from the two data sets, we found that our tool can achieve model performance with the big data set (2,064 Marcellus wells) with only four predictor variables that is similar to that with the small data set (249 Bakken wells) with six predictor variables. This implies that, for big data sets, even with a limited number of available predictor variables, our tool can still be very effective in identifying hot zones that would yield highly productive wells. The data sets that we have access to in this study contain very limited completion, geological, and petrophysical information. Results from this study clearly demonstrated that the data-analysis tool is certainly powerful and flexible enough to take advantage of any additional engineering and geology data to allow the operators to gain insights on the impact of these factors on well performance.
Fault Big Data Analysis Tool based on Deep Learning